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Alfian Ma'arif
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International Journal of Robotics and Control Systems
Published by Association for Scientific Computing Electronics and Engineering
ISSN : -     EISSN : 27752658     DOI : https://doi.org/10.31763/ijrcs
Core Subject : Engineering,
Control & Systems Engineering Electrical & Electronics Engineering
International Journal of Robotics and Control Systems is open access and peer-reviewed international journal that invited academicians (students and lecturers), researchers, scientists, and engineers to exchange and disseminate their work, development, and contribution in the area of robotics and control technology systems experts. Its scope includes Industrial Robots, Humanoid Robot, Flying Robot, Mobile Robot, Proportional-Integral-Derivative (PID) Controller, Feedback Control, Linear Control (Compensator, State Feedback, Servo State Feedback, Observer, etc.), Nonlinear Control (Feedback Linearization, Sliding Mode Controller, Backstepping, etc.), Robust Control, Adaptive Control (Model Reference Adaptive Control, etc.), Geometry Control, Intelligent Control (Fuzzy Logic Controller (FLC), Neural Network Control), Power Electronic Control, Artificial Intelligence, Embedded Systems, Internet of Things (IoT) in Control and Robot, Network Control System, Controller Optimization (Linear Quadratic Regulator (LQR), Coefficient Diagram Method, Metaheuristic Algorithm, etc.), Modelling and Identification System.
Arjuna Subject : Teknik (semua kategori) - Teknik Kontrol dan Sistem
Articles 26 Documents
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Search results for , issue "Vol 4, No 4 (2024)" : 26 Documents clear
Altitude Controller Based on Artificial Neural Network Genetic Algorithm for a Quadcopter MAV Ibarra, José Ramón Meza; Ulloa, Joaquın Martınez; Pacheco, Luis Alfonso Moreno; Cortes, Hugo Rodrıguez
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1582

Abstract

Mechanical systems with high dynamic complexity often face challenges due to unmodeled uncertainties and external perturbations, making effective control difficult. Therefore, new advanced, robust, intelligent control theories have been developed through the sudden advance of computational power in recent years. In this research work, these new theories of automatic control are used, mainly based on what is currently called Artificial Intelligence (AI) algorithms, to develop a novel altitude controller based on the theory of Genetic Algorithms (GA) and Artificial Neural Networks (ANN).Theperformance of the designed controller is evaluated by employing the numerical simulation model in MATLAB SIMULINK, which was created for the commercial MAV Mambo Parrot. The developed intelligent ANN-GA controller uses the Levenberg-Marquardt optimization method and a Genetic Algorithm (GA) to improve Artificial Neural Network performance. The initial PID gains are obtained according to the GA, generating optimal values that initialize the neural network and contribute to optimal performance of the ANN training through evaluation of (Mean Square Error) MSE and (Integral Time Absolute Error) ITAE; the ANN takes then, the adequate output and signals as data from input to calculate the required combination of gains as output for MAV altitude controller. Simulation results demonstrate that the self-tunable controller improves the settling time, decreasing by 31.6% compared to the original PID controller. The certainty of the implemented controller opens new routes for automatic control strategies based on artificial intelligence algorithms for the complex nonlinear dynamics of unmanned aircraft.
Application of Artificial Neural Networks in Predicting Internal Combustion Engine Performance and Emission Characteristics: A Review of Key Methodologies and Findings Mohasab, Hamada; Abouelsoud, Mostafa; Shmroukh, Ahmed N.; Ghazaly, Nouby
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1584

Abstract

The global need for fuel-efficient coupled with minimizing the environmental impacts of ICEs. This review paper highlights how different ANN methodologies such as backpropagation, recurrent neural networks (RNNs), and long short-term memory (LSTM) networks have been applied to optimize engine calibration, improve fuel efficiency, and minimize emissions across a wide range of fuel blends, including hydrogen-gasoline and ethanol-gasoline mixtures. The research focuses on the application of ANN models to predict performance indicators such as brake thermal efficiency, brake-specific fuel consumption, and emissions, reducing reliance on costly and time-consuming experimental tests. The methodology involved a systematic review of peer-reviewed studies published between 2010 and 2024. Studies were selected based on criteria such as relevance to ICE performance and emission control, use of ANN methodologies, and the availability of experimental or simulation data for validation. involves the use of advanced ANN architectures, including backpropagation, RNNs, and LSTM networks, to establish nonlinear relationships between input parameters such as engine speed, load, and fuel type, and output performance indicators. Findings show that comparison between real model and developed program enhanced from ANN model make a difference prediction capability for engine performance enhanced by at least 10 to 15 % of the traditional modeling. techniques, provide better calibration method of ICEs for better fuel consumption. efficiency and reduced emissions. This present study seeks to establish itself in matters that have not been explored in other papers or researches as follows. integration of Hybrid ANN models, which are better than conventional methods in two major trends, one of which is the improvement of the predictive accuracy and the other is the achievement of increased computational efficiency. It is found that the ANN methodologies presents a strong armory in improving the performance of ICE coupled with lowering of emissions with the possibilities of additions for further enhancements of the technology through the incorporation of other machines use of learning techniques in the future studies.
Sensorless Speed Estimation Basing on MRAS Model for a PMSM Machine Application Elnaggar, Mohamed F.; Aymen, Flah; Mourad, Dina
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1585

Abstract

Wind energy systems utilizing synchronous machines can encounter challenges with speed detection at high rotational speeds due to increasing motor temperatures affecting parameters like stator resistance. This paper addresses these challenges by proposing a novel high-speed estimator algorithm based on the Model Reference Adaptive System (MRAS) approach. The primary contribution of this research is the development of an MRAS-based speed estimator that leverages a reactive power model to maintain robustness against variations in stator resistance, even at elevated speeds. To optimize the estimator’s performance, we employed a particle optimization algorithm for tuning, which overcomes issues related to regulator parameter identification. We implemented the proposed algorithm in Matlab and validated it on a real machine prototype capable of high-speed operation. After a comparison wth 5 different methods, the results indicate that the estimator performs effectively up to 42,000 RPM (600 Hz), demonstrating a maximum speed estimation error of 50 Hz. Stability analyses across various speed regions and practical lab tests confirm the robustness and accuracy of the proposed control scheme. The findings highlight the estimator’s improved performance in high-speed scenarios, showcasing its potential for enhancing speed detection in wind energy systems.
Adaptive Fuzzy Logic Control of Quadrotor Yasmine, Zamoum; Karim, Baiche; Razika, Boushaki; Younes, Benrabah
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1583

Abstract

Intelligent controllers are created in this work to regulate the attitude of quadrotor UAVs (Unmanned Aerial Vehicles). Quadrotors offer a wide range of real-time applications, including surveillance, inspection, search and rescue, and lowering the human force safety risks. The kinematics of quadrotor are similar to those of an inverted pendulum. To maintain balance, they must continuously adjust orientation and thrust. External disturbances, like wind or sudden movements, can easily destabilize them, necessitating sophisticated control algorithms for stable flight and precise maneuverability. This instability poses a significant challenge in designing and operating quadrotors, especially in dynamic environments where real-time adjustments are crucial for maintaining control. To avoid any form of damage, a mathematical model should be constructed first, followed by the implementation of various control systems. A thorough simulation model for a Quadrotor is presented in this project. The quadrotor is a six degrees of freedom object, it has six variables to express its position in space where (x, y and z) represent the distance of quadrotor from an earth fixed inertial form to its center of mass, main movements of roll, pitch, yaw are the Euler angles representing the orientation of the quadrotor at each axis. The proposed control techniques are applied separately: PID Controller, Fuzzy Logic PID Controller and Adaptive Fuzzy Logic PID Controller. The purpose of this work is to asses these control techniques for the motions of a Quadrotor in terms of better performance, tracking error reduction, and stability. MATLAB software is used for modeling, control, and simulation. According to the obtained results, the PID controller provided the best settling time. In addition, when we applied fuzzy logic PID control to adjust the pitch angle, the system experienced overshoot; however, with Adaptive Fuzzy Logic PID controller, the system provided the best performance according to the desired criteria.
Lightning Risk Assessment, Control and Protection Scheme Design for a Rooftop Photovoltaic System in the New Capital of Egypt Omar, Ahmed I.; Abd-Allah, M. A.; Shokry, Ahmed; Said, Abdelrahman
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1525

Abstract

The absence of an effective lightning protection system for photovoltaic (PV) systems can hinder their integration into networks. Outdoor PV installations are vulnerable to direct or indirect lightning strikes, resulting in damaging overvoltages that harm the PV structure. These systems, often situated on rooftops or open fields, face increased lightning strike risks due to their exposure compared to more sheltered setups. Lightning-induced surges can harm sensitive electrical components like panels, inverters, and wiring, leading to potential damage and downtime. The complexity of PV systems, with interconnected components, makes designing protection strategies challenging. Compliance with lightning protection standards is crucial to prevent damage, downtime, and financial losses. Implementing effective protection measures involves grounding, surge protection, and adherence to regulations. Lightning protection systems intercept strikes and safely direct electrical energy to the ground, safeguarding sensitive components and ensuring continuous power generation. The IEC 62305-2 standard guides lightning risk assessment and mitigation, aiding in evaluating risks, calculating damage likelihood, and designing protective measures. A case study focusing on the Arab African International Bank's rooftop PV system in Egypt illustrates the importance of lightning risk management in financial, operational, and regulatory contexts for solar projects. Risk assessment aims to identify vulnerabilities, implement mitigation strategies, and ensure safe, reliable system operation. By addressing lightning risks effectively, stakeholders can enhance system safety, reliability, and longevity while minimizing downtime and revenue loss associated with lightning strikes.
A Systematic Review of the Use of Technology in Educational Assessment Practices: Lesson Learned and Direction for Future Studies Retnawati, Heri; Kardanova, Elena; Sumaryanto, Sumaryanto; Prasojo, Lantip Diat; Jailani, Jailani; Arliani, Elly; Hidayati, Kana; Susanti, Mathilda; Lestari, Himmawati Puji; Apino, Ezi; Rafi, Ibnu; Rosyada, Munaya Nikma; Tuanaya, Rugaya; Dewanti, Septinda Rima; Sotlikova, Rimajon; Kassymova, Gulzhaina Kuralbayevna
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1572

Abstract

Previous studies have demonstrated that technology helps achieve learning outcomes. However, many studies focus on just one aspect of technology’s role in educational assessment practices, leaving a gap in studies that examine how various aspects affect the use of technology in assessments. Hence, through a systematic work, we analyzed the extent and manner in which technology is integrated into educational assessments and how education level, domain of learning, and region may affect the use of technology. We reviewed empirical studies from two major databases (i.e., Scopus and ERIC) and a national journal whose focus and scope are on educational measurement and assessment, following PRISMA guidelines for systematic reviews. The findings of the present study are directed towards emphasizing the roles of technology in educational assessment practices and how these roles are adapted to varying educational contexts such as the level of education, the three domains of learning (i.e., cognitive, psychomotor, and affective), and the setting in which the assessment was conducted. These findings not only highlight the current roles of technology in educational assessment but also provide a roadmap for future research aimed at optimizing the integration of technology across diverse educational contexts.
NMPC Based-Trajectory Tracking and Obstacle Avoidance for Mobile Robots Qasim, Mohammed Salim; Ayoub, Abdurahman Basil; Abdulla, Abdulla Ibrahim
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1605

Abstract

This paper presents the design of a Nonlinear Model Predictive Controller (NMPC) for a wheeled Omnidirectional Mobile Robot (OMR) in order to track a desired trajectory in the presence of previously unknown static and dynamic obstacles in the environment around the robot. A laser rangefinder sensor is used to detect the obstacles where each obstacle occupies numerous points of every sensor reading. The points that belong to each obstacle are then clustered together using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. This research introduces a novel approach to represent obstacles as multiple rotated ellipses, enabling a more accurate representation of complex obstacle shapes without overestimating their boundaries, thereby allowing the robot to navigate through narrow passages. CoppeliaSim robotic simulator is utilized to create the virtual simulation environment as well as simulate the OMR dynamics. MATLAB with the help of the CasADi toolbox is used for the process of the laser rangefinder readings and the implementation of NMPC, respectively.  To validate the effectiveness and robustness of the proposed approach, three simulation scenarios are conducted, each involving distinct trajectories and varying densities of static and/or dynamic obstacles. The proposed control architecture exhibits remarkable performance, enabling the OMR to effectively navigate through narrow passages and avoid multiple static and dynamic obstacles while closely adhering to the desired trajectory.
A Review of Deep Learning-Based Defect Detection and Panel Localization for Photovoltaic Panel Surveillance System Mohamed Ameerdin, Muhammad Irshat; Jamaluddin, Muhammad Herman; Shukor, Ahmad Zaki; Mohamad, Syazwani
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1579

Abstract

As the photovoltaic (PV) systems expands globally, robust defect detection and precise localization technologies becomes crucial to ensure their operational efficiency. This review introduces an integrated deep learning framework that leverages Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and You Only Look Once (YOLO) algorithms to enhance defect detection in solar panels. By integrating these technologies with Global Positioning System (GPS) and Real-Time Kinematic (RTK) GPS, the framework achieves unprecedented accuracy in defect localization, facilitating efficient maintenance and monitoring of expansive solar farms. Specifically, CNNs are employed for their superior feature detection capabilities in identifying defects such as microcracks and delamination. RNNs enhance the framework by analyzing time-series data from panel sensors, predicting potential failure points before they manifest. YOLO algorithms are utilized for their real-time detection capabilities, allowing for immediate identification and categorization of defects during routine inspections. This review's novel contribution lies in its use of an integrated approach that combines these advanced technologies to not only detect but also accurately localize defects, significantly impacting the maintenance strategies for PV systems. The findings demonstrate an improvement in detection speed and localization accuracy, suggesting a promising direction for future research in solar panel diagnostics. The review provided aims to refine surveillance systems and improve the maintenance protocols for photovoltaic installations, ensuring longevity, durability and efficiency in energy production.
Enhanced RSA Optimized TID Controller for Frequency Stabilization in a Two-Area Power System Ekinci, Serdar; Eker, Erdal; Izci, Davut; Smerat, Aseel; Abualigah, Laith
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1644

Abstract

This study presents an enhanced reptile search algorithm (ImRSA) optimized tilt-integral-derivative (TID) controller for load frequency control (LFC) in a two-area power system consisting of photovoltaic (PV) and thermal power units. The ImRSA integrates Lévy flight and logarithmic spiral search mechanisms to improve the balance between exploration and exploitation, resulting in more efficient optimization performance. The proposed controller is tested against the original reptile search algorithm (RSA) and other state-of-the-art optimization methods, such as modified grey wolf optimization with cuckoo search, black widow optimization, and gorilla troops optimization. Simulation results show that the ImRSA-optimized TID controller outperforms these approaches in terms of undershoot, overshoot, settling time, and the integral of time-weighted absolute error metric. Additionally, the ImRSA demonstrates robustness in managing frequency deviations caused by solar radiation fluctuations in PV systems. The results highlight the superior efficiency and reliability of the proposed method, especially for renewable energy integration in modern power systems.
Dynamic Assessment and Control of a Dual Star Induction Machine State Dedicated to an Electric Vehicle Under Short-Circuit Defect Benbouya, Basma; Cheghib, Hocine; Behim, Meriem; Mahmoud, Mohamed Metwally; Elnaggar, Mohamed F.; Ibrahim, Nagwa F.; Anwer, Noha
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1557

Abstract

The widespread use of electric vehicles (EVs) in several industries gives rise to many significant safety and reliability-related issues. Thus, there is a need for methods for identifying flaws in EV components. In this paper, a state assessment of a dual star induction machine (DSIM) under short-circuit faults is investigated. The DSIM is selected due to its widespread use in high-power applications and its numerous advantages over other conventional machine types. Our focus is particularly on its application in the automotive industry, where its dual stator windings ensure reliable and robust parallel operation, thereby enhancing its robustness and efficiency. To improve this technology and ensure its proper functioning following potential failures and during maintenance, appropriate diagnostic and monitoring methods are essential. Our methodology combines two techniques: the current space vector (CSV), utilized to prevent information loss, and the wavelet packet decomposition energy, calculated from the resulting CSV signals. This approach enables the detection of various stator short-circuit faults, presenting different severities and occurring at different locations. The outcomes of this study, which were verified through the use of a Simulink model of a DSIM devoted to an EV, showcase the efficacy of the suggested approach. Furthermore, this work underscores the significance of this approach in maintaining the performance and reliability of DSIM, particularly in demanding environments such as the automotive industry.

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